In recent years, a large amount of information has been circulating throughout the world via Web pages, electronic bulletin boards and the like on the Internet. Accordingly, it is becoming more difficult for an ordinary user to determine which pieces of information are reliable or not on the Internet.
For example, when one proposition, such as “green tea works well for cancer” or “Pluto is a planet,” is picked up, a large amount of information about arguments for and against the proposition, including various articles, comments and opinions about the proposition, is circulating on the Internet. Therefore, a user can judge the credibility of the proposition by referring to a wide range of such information. However, the judgment may be biased by referring to only a part of the information. Meanwhile, in reality, it is extremely difficult for a user to go through all the information. In particular, the trend grows stronger as the credibility of the information changes with time.
For example, the credibility of the proposition that “Pluto is a planet” was changed in 2006, when the definition of what a planet is was changed. There is risk that the user's judgment may vary depending on when the information that the user refers to is posted.
The related technique for evaluating the credibility of information on the Internet is, for example, disclosed in NPL 1. According to the related technique disclosed in NPL 1, a large amount of Web documents including a specific proposition is classified by sender of document, approval or disapproval opinion of document, and meaning such as factual grounding before being presented, thereby helping a user judge the credibility concerning the proposition.
In many cases, time information, such as a date and time of creation or a date or time of sending, is added to articles, blogs, emails and the like on the Internet. There is a technique of extracting from information on the Internet documents including the proposition on which attention is focused, sorting the extracted documents by time information attached to each document, and presenting the time when the proposition on which attention is focused appears and the number of times that the proposition appears. For example, what is disclosed in NPL 2 is a method of extracting and displaying the time-series changes in the number of times that a specific topic word appears in the whole of collected blogs.